13,716
edits
m (Remove links to pages that are actually redirects to this page.) |
m (Remove links to pages that are actually redirects to this page.) |
||
Line 28: | Line 28: | ||
=== 2014 === | === 2014 === | ||
* ([[Rei & Briscoe, 2014]]) ⇒ [[Marek Rei]], and [[Ted Briscoe]]. ([[2014]]). “[http://www.aclweb.org/anthology/W14-1608 Looking for Hyponyms in Vector Space].” In: Proceedings of CoNLL-2014. | * ([[Rei & Briscoe, 2014]]) ⇒ [[Marek Rei]], and [[Ted Briscoe]]. ([[2014]]). “[http://www.aclweb.org/anthology/W14-1608 Looking for Hyponyms in Vector Space].” In: Proceedings of CoNLL-2014. | ||
** QUOTE: [[Word2vec]]: [[We]] created word representations using the [[word2vec-like System|word2vec toolkit]]<ref>https://code.google.com/p/word2vec/</ref>. </s> The tool is based on a [[feedforward neural network language model]], with modifications to make [[representation learning]] more efficient ([[Mikolov et al., 2013a]]). </s> [[We]] make use of the [[skip-gram model]], which takes each [[word in a sequence]] as an input to a [[log-linear classifier]] with a [[continuous projection layer]], and [[predicts word]]s within a [[text window|certain range before and after the input word]]. </s> The [[text window size|window size]] was set to 5 and [[vector]]s were trained with both [[100]] and 500 dimensions. </s> | ** QUOTE: [[word2vec-like System|Word2vec]]: [[We]] created word representations using the [[word2vec-like System|word2vec toolkit]]<ref>https://code.google.com/p/word2vec/</ref>. </s> The tool is based on a [[feedforward neural network language model]], with modifications to make [[representation learning]] more efficient ([[Mikolov et al., 2013a]]). </s> [[We]] make use of the [[skip-gram model]], which takes each [[word in a sequence]] as an input to a [[log-linear classifier]] with a [[continuous projection layer]], and [[predicts word]]s within a [[text window|certain range before and after the input word]]. </s> The [[text window size|window size]] was set to 5 and [[vector]]s were trained with both [[100]] and 500 dimensions. </s> | ||
=== 2013 === | === 2013 === |
edits